Mn-based cGAS-STING activation for tumor therapy

Immunotherapy has efficiently revolutionized the treatment of human neoplastic diseases.However,the overall responsive rate of current immunotherapy is still unsatisfactory,benefiting only a small proportion of patients.Therefore,significant attention has been paid to the modulation of tumor microenvironment(TME)for the enhancement of immunotherapy.Interestingly,recent studies have shown that cyclic GMP-AMP synthasestimulator of interferon gene(cGAS-STING)was initially found as an innate immune sensor to recognize cytoplasmic DNA(such as bacterial,viral,micronuclei,and mitochondrial).It is a promising signaling pathway to activate antitumor immune responses via type I interferon production.Notably,Mn^(2+)was found to be a critical molecule to sensitize the activation of the cGAS-STING pathway for better immunotherapy.This activation led to the development of Mn^(2+)-based strategies for tumor immunotherapy via the activation of the cGAS-STING pathway.In this critical review,we aimed to summarize the recent progress of this field,focusing on the following three aspects.First,we briefly introduced the signaling pathway of cGAS-STING activation,and its regulation effect on the antitumor immunity cycle has been discussed.Along with this,several agonists of the cGAS-STING pathway were introduced with their potential as immunotherapeutic drugs.Then,the basic biological functions of Mn^(2+)have been illustrated,focusing on its critical roles in the cGAS-STING pathway activation.Next,we systematically reviewed the Mn^(2+)-based strategies for tumor immunotherapy,which can be classified by the methods based on Mn^(2+)alone or Mn^(2+)combined with other therapeutic modalities.We finally speculated the future perspectives of the field and provided rational suggestions to develop better Mn^(2+)-based therapeutics.

Delay-Optimal Random Access in Large-Scale Energy Harvesting IoT Networks Based on Mean Field Game

With energy harvesting capability, the Internet of things(IoT) devices transmit data depending on their available energy, which leads to a more complicated coupling and brings new technical challenges to delay optimization. In this paper,we study the delay-optimal random access(RA) in large-scale energy harvesting IoT networks. We model a two-dimensional Markov decision process(MDP)to address the coupling between the data and energy queues, and adopt the mean field game(MFG) theory to reveal the coupling among the devices by utilizing the large-scale property. Specifically, to obtain the optimal access strategy for each device, we derive the Hamilton-Jacobi-Bellman(HJB) equation which requires the statistical information of other devices.Moreover, to model the evolution of the states distribution in the system, we derive the Fokker-PlanckKolmogorov(FPK) equation based on the access strategy of devices. By solving the two coupled equations,we obtain the delay-optimal random access solution in an iterative manner with Lax-Friedrichs method. Finally, the simulation results show that the proposed scheme achieves significant performance gain compared with the conventional schemes.

Adaptive beaconing for collision avoidance and tracking accuracy in vehicular networks

In vehicular networks,the exchange of beacons among neighboring vehicles is a promising solution to guarantee a vehicle's safety.However,frequent beaconing under high vehicle density conditions will cause beacon collisions,which are harmful to a vehicle's driving safety and the location tracking accuracy.We propose an ABIwRC(Adaptive Beaconing Interval with Resource Coordination)method for a highway scenario.Each vehicle broadcasts beacon interval requests,including the intervals needed for both the vehicle's driving safety and location tracking accuracy.The RSU(Road Side Unit)allocates resources for a vehicle's beaconing according to the requests from all vehicles and the interference relationship between the vehicles in adjacent RSUs.We formulate a resource allocation problem for maximizing the sum utility,which measures the satisfaction of vehicles'requests.We then transform the optimization problem into a maximum weighted independent set problem,and propose an algorithm to solve this effciently.Simulation results show that the proposed method outperforms the benchmark in terms of beacon reception ratio,vehicle driving safety,and location tracking accuracy.